Introduced in [1]}, binarized neural networks are multilayer neural networks
whose activations and weights are predominantly binary (but ranging in \(\lbrace -1,1\rbrace \) ).
A BNN is usually described in terms of composition of \(d\)  blocks of layers
(that are assembled sequentially) rather than individual layers. Thus, a BNN \(N\)  consists of a number (say, \(m = d-1\) ) of internal blocks, followed by a unique output block, noted \(O\) .
Each block consists of a collection of linear and non-linear transformations. The \(k\) th internal block \(BLK_k\)  (\(k \in [m]\) ) in a BNN can be modeled as a mapping
\(BLK_k: \lbrace -1,1\rbrace ^{n_k} \rightarrow \lbrace -1,1\rbrace ^{n_{k+1}} \) 
